Longitudinal exposure to antiseizure medications shape gut-derived microbiome, resistome, and metabolome landscape
Bibliographic record
Abstract
Abstract The influence of chronically administered host-targeted drugs on the gut microbiome remains less understood compared to antibiotics. We investigated repetitive exposure effects of three common antiseizure medications [carbamazepine (CBZ), valproic acid, and levetiracetam] on the gut microbial composition, resistome, and metabolome using microcosms constructed from feces of young children. Microcosms were established by cultivating feces for 24 h (C0). These microcosms were daily transferred into fresh media for seven cycles (C1–C7) with antiseizure medications or carrier molecules, followed by four cycles without any drugs (C8–C11). The microbial dynamics and resistome of microcosms at C0, C1, C7, and C11 were assessed with 16S ribosomal ribonucleic acid gene sequencing or shotgun metagenome sequencing and real-time quantitative polymerase chain reaction analysis of the antimicrobial resistance genes, respectively. Metabolites of CBZ-treated and control microcosms at C0, C1, and C7 were evaluated using non-targeted metabolomics. Our findings revealed that the serial transfer approach longitudinally altered the microcosm composition. Among the medications, CBZ had the most substantial impact on the structure and metabolism of the feces-derived microcosms. The microbiome composition partially recovered during the drug-free period. Specifically, Bacteroides and Flavonifractor were depleted and Escherichia and Clostridium were enriched. Additionally, repetitive CBZ exposure increased the abundance and expression of genes related to various antibiotic resistance mechanisms, more specifically, efflux pumps and antibiotic target alteration. CBZ-induced changes in the microbiome were mirrored in the metabolome, with reductions in the citric acid cycle metabolites, glutamine, and spermidine, alongside increased levels of vitamin B6. Our study suggests that repetitive CBZ exposure may negatively impact gut microbial homeostasis and metabolism.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".